The Robotics Revolution, with Physical Intelligence’s Cofounder Chelsea Finn

**Elad Gil** (0:05)
Hi, listeners. Welcome to No Priors. This week, we're speaking to Chelsea Finn, co-founder of Physical Intelligence, a company bringing general purpose AI into the physical world. Chelsea co-founded Physical Intelligence alongside a team of leading researchers and minds in the field. She's an Associate Professor of Computer Science and Electrical Engineering at Stanford University, and prior to that, she worked at Google Brain and was at Berkeley. Chelsea's research focuses on how AI systems can acquire general purpose skills through interactions with the world. Chelsea, thank you so much for joining us today on No Priors.

**Chelsea Finn** (0:33)
Yeah, thanks for having me.

**Elad Gil** (0:34)
You've done a lot of really important storied work in robotics between your work at Google, at Stanford, etc. I would just love to hear a little bit firsthand your background in terms of your path in the world of robotics. What drew you to it initially and some of the work that you've done?

**Chelsea Finn** (0:48)
Yeah, it's been a long road. At the beginning, I was really excited about the impact that robotics could have in the world, but at the same time, I was also really fascinated by this problem of developing perception and intelligence in machines. And robots embody all of that. And also sometimes there's some cool math that you can do as well that keeps your brain active, makes you think. And so I think all of that is really fun about working in the field. I started working more seriously in robotics more than 10 years ago at this point, at the start of my Ph.D. at Berkeley. And we were working on neural network control, trying to train neural networks that map from image pixels to directly actually to motor torques on a robot arm. At the time, this was not very popular. And we've come a long way, and it's a lot more accepted in robotics, and also just generally something that a lot of people are excited about. Since that beginning point, it was very clear to me that we could train robots to do pretty cool things. But that getting the robot to do one of those things in many scenarios with many objects was a major, major challenge. So 10 years ago, we were training robots to screw a cup onto a bottle, and use a spatula to lift an object into a bowl, and do a tight insertion or hang up a hanger on a clothes rack. It's pretty cool stuff. But actually getting the robot to do that in many environments with many objects, that's where a big part of the challenge comes in.
I've been thinking about ways to make broader data sets, train on those broader data sets, and also different approaches for learning, whether it be reinforcement learning, video prediction, imitation learning, all those things. So yeah, moved from spent a year at Google Brain, in between my PhD and joining Stanford, became a professor at Stanford, started a lab there, did a lot of work along all these lines, and then recently started Physical Intelligence almost a year ago at this point. So I've been on leave from Stanford for that, and it's been really exciting to be able to try to execute on the vision that the co-founders that we collectively have, and do it with a lot of resources and so forth. And I'm also still advising students at Stanford as well.

**Elad Gil** (3:09)
That's really cool. And I guess you started Physical Intelligence with four other co-founders and an incredibly impressive team. Could you tell us a little bit more about what Physical Intelligence is working on in the approach that you're taking? Because I think it's a pretty unique slant on the whole field and approach.

**Chelsea Finn** (3:21)
Yeah. So we're trying to build a big neural network model that could ultimately control any robot to do anything in any scenario. And a big part of our vision is that in the past robotics has focused on trying to go deep on one application and developing a robot to do one thing, and then ultimately gotten kind of stuck in that one application. It's really hard to solve one thing and then try to get out of that and broaden. And instead, we're really in it for the long term to try to address this broader problem of physical intelligence in the real world. We're thinking a lot about generalization, generalists, and unlike other robotics companies, we think that being able to leverage all of the possible data is very important. And this comes down to actually not just leveraging data from one robot, but from any robot platform that might have six joints or seven joints or two arms or one arm. We've seen a lot of evidence that you could actually transfer a lot of rich information across these different embodiments and allows you to use data. And also if you iterate on your robot platform, you don't have to throw all your data away. I have faced a lot of pain in the past where we got a new version of the robot and then your policy doesn't work. And it's a really painful process to try to get back to where you were on the previous robot iteration. So yeah, trying to build generalist robots and essentially kind of develop foundation models that will power the next generation of robots in the real world.